Much research has been done in the area of automating the generation of these sounds through the aid of machine learning and neural networks. Common approaches included directly generating the waveform in the time domain [1]} or predicting synthesis parameters based on hand-picked analysis features [2]}. In their 2020 paper on Differentiable Digital Signal Processing (DDSP)[3]}, Engel et al. proposed a novel approach to neural audio synthesis. Rather than generating signals directly in the time or frequency domain, DDSP offers a complete end-to-end toolbox consisting of a synthesizer based on Spectral Modeling Synthesis (SMS) [4]}, and an autoencoder neural network architecture that takes care of both extracting analysis features and predicting synthesis parameters.
